


"""simple docstring"""


import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase   :Optional[int]						     =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase   :Union[str, Any]						     =		[]
for i in range(6):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
    )
    rename_keys.append(
        (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
    )
    rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
    rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
    rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
    rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
    rename_keys.append(
        (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
    )
    rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
    rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
    rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
    )
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
    )
    rename_keys.append(
        (
            f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
            f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
        )
    )
    rename_keys.append(
        (
            f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
            f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
        )
    )
    rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
    rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
    rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
    rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
    )
    rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
    )
    rename_keys.append(
        (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
    )
    rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
    rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
        ('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
    ]
)




def 	snake_case		(  UpperCamelCase__							:    Optional[int]      ,					UpperCamelCase__							:    Dict      ,					UpperCamelCase__							:    Optional[int]     ) ->   int:
      lowerCamelCase       :       List[Any]   =						state_dict.pop(_lowerCamelCase     )
      lowerCamelCase       :       Any   =						val




def 	snake_case		(  UpperCamelCase__							:    Dict     ) ->   List[str]:
      lowerCamelCase       :       Optional[Any]   =						OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  lowerCamelCase       :       Dict   =						key.replace("""backbone.0.body"""      ,					"""backbone.conv_encoder.model"""     )
                  lowerCamelCase       :       int   =						value
            else:
                  lowerCamelCase       :       Optional[int]   =						value

      return new_state_dict




def 	snake_case		(  UpperCamelCase__							:    Any     ) ->   str:
      lowerCamelCase       :       Tuple   =						""""""

      # first: transformer encoder
      for i in range(6     ):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            lowerCamelCase       :       List[str]   =						state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'     )
            lowerCamelCase       :       List[Any]   =						state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'     )
            # next, add query, keys and values (in that order) to the state dict
            lowerCamelCase       :       Tuple   =						in_proj_weight[:256, :]
            lowerCamelCase       :       Optional[int]   =						in_proj_bias[:256]
            lowerCamelCase       :       Optional[int]   =						in_proj_weight[256:512, :]
            lowerCamelCase       :       Dict   =						in_proj_bias[256:512]
            lowerCamelCase       :       Tuple   =						in_proj_weight[-256:, :]
            lowerCamelCase       :       Optional[int]   =						in_proj_bias[-256:]
      # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
      for i in range(6     ):
            # read in weights + bias of input projection layer of self-attention
            lowerCamelCase       :       Optional[Any]   =						state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'     )
            lowerCamelCase       :       Tuple   =						state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'     )
            # next, add query, keys and values (in that order) to the state dict
            lowerCamelCase       :       int   =						in_proj_weight[:256, :]
            lowerCamelCase       :       Union[str, Any]   =						in_proj_bias[:256]
            lowerCamelCase       :       Tuple   =						in_proj_weight[256:512, :]
            lowerCamelCase       :       Optional[int]   =						in_proj_bias[256:512]
            lowerCamelCase       :       str   =						in_proj_weight[-256:, :]
            lowerCamelCase       :       Union[str, Any]   =						in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            lowerCamelCase       :       Any   =						state_dict.pop(
                F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'     )
            lowerCamelCase       :       int   =						state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'     )
            # next, add query, keys and values (in that order) of cross-attention to the state dict
            lowerCamelCase       :       str   =						in_proj_weight_cross_attn[:256, :]
            lowerCamelCase       :       Union[str, Any]   =						in_proj_bias_cross_attn[:256]
            lowerCamelCase       :       str   =						in_proj_weight_cross_attn[256:512, :]
            lowerCamelCase       :       List[Any]   =						in_proj_bias_cross_attn[256:512]
            lowerCamelCase       :       Any   =						in_proj_weight_cross_attn[-256:, :]
            lowerCamelCase       :       List[str]   =						in_proj_bias_cross_attn[-256:]




def 	snake_case		(  UpperCamelCase__							:    Union[str, Any]      ,					UpperCamelCase__							:    Any     ) ->   Any:
      lowerCamelCase  ,      lowerCamelCase       :       str   =						image.size
      lowerCamelCase       :       int   =						max(_lowerCamelCase      ,					_lowerCamelCase     )
      lowerCamelCase       :       Union[str, Any]   =						800 if """detection""" in checkpoint_url else 1000
      lowerCamelCase       :       Optional[int]   =						target_max_size / current_max_size
      lowerCamelCase       :       Dict   =						image.resize((int(round(scale * width     )     ), int(round(scale * height     )     ))     )

      return resized_image




def 	snake_case		(  UpperCamelCase__							:    int     ) ->   str:
      lowerCamelCase       :       Tuple   =						F.to_tensor(_lowerCamelCase     )
      lowerCamelCase       :       Optional[Any]   =						F.normalize(_lowerCamelCase      ,					mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]      ,					std=[0.2_2_9, 0.2_2_4, 0.2_2_5]     )
      return image




@torch.no_grad()
def 	snake_case		(  UpperCamelCase__							:    Optional[int]      ,					UpperCamelCase__							:    Optional[int]      ,					UpperCamelCase__							:    Any     ) ->   List[Any]:
      logger.info("""Converting model..."""     )

      # load original state dict
      lowerCamelCase       :       int   =						torch.hub.load_state_dict_from_url(_lowerCamelCase      ,					map_location="""cpu"""     )
      # rename keys
      for src, dest in rename_keys:
            rename_key(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase     )
      lowerCamelCase       :       Optional[int]   =						rename_backbone_keys(_lowerCamelCase     )
      # query, key and value matrices need special treatment
      read_in_q_k_v(_lowerCamelCase     )
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      lowerCamelCase       :       Optional[int]   =						"""model."""
      for key in state_dict.copy().keys():
            if not key.startswith("""class_labels_classifier"""     ) and not key.startswith("""bbox_predictor"""     ):
                  lowerCamelCase       :       Tuple   =						state_dict.pop(_lowerCamelCase     )
                  lowerCamelCase       :       Optional[int]   =						val
            # create HuggingFace model and load state dict
      lowerCamelCase       :       str   =						TableTransformerConfig(
          backbone="""resnet18"""      ,					mask_loss_coefficient=1      ,					dice_loss_coefficient=1      ,					ce_loss_coefficient=1      ,					bbox_loss_coefficient=5      ,					giou_loss_coefficient=2      ,					eos_coefficient=0.4      ,					class_cost=1      ,					bbox_cost=5      ,					giou_cost=2      ,					)

      if "detection" in checkpoint_url:
            lowerCamelCase       :       Optional[Any]   =						15
            lowerCamelCase       :       Optional[int]   =						2
            lowerCamelCase       :       List[str]   =						{0: """table""", 1: """table rotated"""}
            lowerCamelCase       :       int   =						idalabel
            lowerCamelCase       :       Dict   =						{v: k for k, v in idalabel.items()}
      else:
            lowerCamelCase       :       int   =						125
            lowerCamelCase       :       Optional[int]   =						6
            lowerCamelCase       :       List[str]   =						{
                0: """table""",
                1: """table column""",
                2: """table row""",
                3: """table column header""",
                4: """table projected row header""",
                5: """table spanning cell""",
            }
            lowerCamelCase       :       List[str]   =						idalabel
            lowerCamelCase       :       List[str]   =						{v: k for k, v in idalabel.items()}

      lowerCamelCase       :       List[Any]   =						DetrImageProcessor(
          format="""coco_detection"""      ,					max_size=800 if """detection""" in checkpoint_url else 1000     )
      lowerCamelCase       :       str   =						TableTransformerForObjectDetection(_lowerCamelCase     )
      model.load_state_dict(_lowerCamelCase     )
      model.eval()

      # verify our conversion
      lowerCamelCase       :       Union[str, Any]   =						"""example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
      lowerCamelCase       :       int   =						hf_hub_download(repo_id="""nielsr/example-pdf"""      ,					repo_type="""dataset"""      ,					filename=_lowerCamelCase     )
      lowerCamelCase       :       Optional[Any]   =						Image.open(_lowerCamelCase     ).convert("""RGB"""     )
      lowerCamelCase       :       List[str]   =						normalize(resize(_lowerCamelCase      ,					_lowerCamelCase     )     ).unsqueeze(0     )

      lowerCamelCase       :       List[Any]   =						model(_lowerCamelCase     )

      if "detection" in checkpoint_url:
            lowerCamelCase       :       Optional[Any]   =						(1, 15, 3)
            lowerCamelCase       :       Tuple   =						torch.tensor(
                [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]     )
            lowerCamelCase       :       Any   =						torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]     )

      else:
            lowerCamelCase       :       List[str]   =						(1, 125, 7)
            lowerCamelCase       :       Tuple   =						torch.tensor(
                [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]     )
            lowerCamelCase       :       Union[str, Any]   =						torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]     )

      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, :3, :3]      ,					_lowerCamelCase      ,					atol=1E-4     )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,					_lowerCamelCase      ,					atol=1E-4     )
      print("""Looks ok!"""     )

      if pytorch_dump_folder_path is not None:
            # Save model and image processor
            logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'     )
            Path(_lowerCamelCase     ).mkdir(exist_ok=_lowerCamelCase     )
            model.save_pretrained(_lowerCamelCase     )
            image_processor.save_pretrained(_lowerCamelCase     )

      if push_to_hub:
            # Push model to HF hub
            logger.info("""Pushing model to the hub..."""     )
            lowerCamelCase       :       str   =						(
                """microsoft/table-transformer-detection"""
                if """detection""" in checkpoint_url
                else """microsoft/table-transformer-structure-recognition"""
            )
            model.push_to_hub(_lowerCamelCase     )
            image_processor.push_to_hub(_lowerCamelCase     )


if __name__ == "__main__":
    __lowerCamelCase   :Optional[int]						     =		argparse.ArgumentParser()

    parser.add_argument(
        '--checkpoint_url',
        default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
        type=str,
        choices=[
            'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
            'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
        ],
        help='URL of the Table Transformer checkpoint you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )
    __lowerCamelCase   :Dict						     =		parser.parse_args()
    convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						complex						,							_lowerCamelCase			:						str = "x"						,							_lowerCamelCase			:						float = 10**-10						,							_lowerCamelCase			:						int = 1						,							) ->							complex:
  lowerCamelCase_       	=		symbols(_lowerCamelCase					)
  lowerCamelCase_       	=		lambdify(_lowerCamelCase						,							_lowerCamelCase					)
  lowerCamelCase_       	=		lambdify(_lowerCamelCase						,							diff(_lowerCamelCase						,							_lowerCamelCase					)					)

  lowerCamelCase_       	=		starting_point

  while True:
    if diff_function(_lowerCamelCase					) != 0:
      lowerCamelCase_       	=		prev_guess - multiplicity * func(_lowerCamelCase					) / diff_function(
          _lowerCamelCase					)
    else:
      raise ZeroDivisionError('Could not find root'					) from None

    # Precision is checked by comparing the difference of consecutive guesses
    if abs(next_guess - prev_guess					) < precision:
      return next_guess

    lowerCamelCase_       	=		next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(F'''The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}''')

  # Find root of polynomial
  # Find fourth Root of 5
  print(F'''The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}''')

  # Find value of e
  print(
      '''The root of log(y) - 1 = 0 is ''',
      F'''{newton_raphson('log(y) - 1', 2, variable='y')}''',
  )

  # Exponential Roots
  print(
      '''The root of exp(x) - 1 = 0 is''',
      F'''{newton_raphson('exp(x) - 1', 10, precision=0.005)}''',
  )

  # Find root of cos(x)
  print(F'''The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}''')




